Training a generator unit and a discriminator unit for collision-aware trajectory prediction

1. A system, comprising: an artificial neural network comprising a generator unit, a discriminator unit, and an oracle unit, wherein said generator unit, said discriminator unit, and said oracle unit are executed by a computer, wherein said generator unit is configured to determine a future trajectory of at least one other road user in an environment of a vehicle considering an observed trajectory of the at least one other road user, wherein said discriminator unit is configured to determine whether the determined future trajectory of the at least one other road user is an actual future trajectory of the at least one other road user, and wherein said computer is configured to train said generator unit and said discriminator unit simultaneously with gradient descent, wherein said oracle unit is configured to determine a reward for the determined future trajectory of the at least one other road user considering whether the determined future trajectory of the other road user is collision-free, and wherein said computer is configured to train said generator unit considering the reward determined by the oracle unit.

2. The system according to claim 1 , wherein the other road user is a vulnerable road user.

3. The system according to claim 1 , wherein the generator unit is configured to determine the future trajectory of the at least one other road user considering at least one static object in the environment of the other road user.

4. The system according to claim 3 , wherein the generator unit is configured to determine the future trajectory of the other road user considering the relative location of the at least one static object.

5. The system according to claim 3 , wherein the generator unit is configured to determine the future trajectory of the other road user considering at least one dynamic object in the environment of the other road user.

6. The system according to claim 1 , wherein the generator unit comprises an encoder unit, with said encoder unit configured to map an observed trajectory of the other road user to a common embedding space.

7. The system according to claim 6 , wherein the encoder unit comprises a long short-term memory unit.

8. The system according to claim 6 , wherein the generator unit comprises a decoder unit, with said decoder unit configured to determine the future trajectory of the other road user considering the common embedding space.

9. The system according to claim 8 , wherein the decoder unit comprises a long short-term memory unit.

10. A generator unit trained by the system according to claim 1 .

11. A computer implemented method for training a generator unit and a discriminator unit of an artificial neural network, wherein said generator unit is configured to determine a future trajectory of at least one other road user in the environment of a vehicle user considering an observed trajectory of the at least one other road user, said discriminator unit is configured to determine whether the determined future trajectory of the other road user is an actual future trajectory of the other road user, said artificial neural network includes an oracle unit that is configured to determine a reward for the determined future trajectory of the at least one other road user considering whether the determined future trajectory of the other road user is collision-free, and said generator unit, said discriminator unit, and said oracle unit are executed by a computer, the method comprising the step of: training, by the computer, said generator unit and said discriminator unit, wherein said training is carried out simultaneously with gradient descent, and said training considers the reward determined by the oracle unit.